Optimal Energy Saving Adaptive Cruise Control in Overtaking Scenarios for a Hybrid Electric Vehicle
Bibliographic record
Abstract
The overtaking planning problem plays a crucial role to foster the adaptive cruise control (ACC) technology. It reveals extremely challenging due to critical requirements on the real-time capability of the control system and on conflicting objectives for the longitudinal speed trajectory generated over time for the Following Vehicle (e.g. in terms of maneuver efficiency, passenger comfort, energy economy). In this paper, an approach to solve this problem is proposed by developing an optimal energy saving oriented ACC algorithm for overtaking scenarios considering a hybrid electric vehicle (HEV) as the Following Vehicle. An off-line optimization based on Dynamic Programming (DP) is implemented. The proposed DP formulation aims at controlling the Following Vehicle longitudinal jerk over time to minimize the overall HEV energy consumption throughout the overtaking maneuver. Optimization constraints are considered for the inter-vehicular distance between Leader Vehicle and Following vehicle over time, and for the operational limits of the HEV powertrain components. The developed ACC algorithm is demonstrated achieving up to 4.1% energy saving and significant improvements in terms of passenger comfort in different overtaking scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".